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Redis evolved rapidly, with versions up to 5.x handling all core CRUD commands on a single worker thread, while Redis 6 introduced multi‑threaded network I/O to leverage multi‑core CPUs, yet still processes core business commands single‑threadedly for performance and simplicity, as explained with diagrams.
This article explains how character encoding works—from ASCII and its extensions to Chinese GB2312 and GBK, through Unicode's UCS‑2, UCS‑4, and the versatile UTF‑8—showing why mismatched encodings produce garbled text and why UTF‑8 is the default in Spring Boot.
Redis, a non‑relational database, implements transaction-like behavior using commands such as MULTI, EXEC, DISCARD, and WATCH, allowing queued execution, optimistic locking, and various rollback granularities, though its semantics differ from traditional relational database transactions.
This article investigates whether the JVM terminates when a thread encounters an OutOfMemoryError, demonstrating through Spring Boot controllers and multithreaded examples how main and child threads, daemon settings, and explicit OOM handling affect JVM shutdown behavior.
This article breaks down zero‑copy data transfer techniques—write+read, mmap+write, sendfile, sendfile + SG‑DMA, and splice—showing how they reduce CPU copies and context switches to boost I/O performance in modern operating systems.
This article explains the concept of distributed locks and compares three common implementation approaches—using a database, Redis, and ZooKeeper—detailing their mechanisms, advantages, drawbacks, and suitable scenarios for ensuring consistent access to shared resources in distributed systems.
One‑click mobile number login streamlines user authentication by leveraging carrier‑provided phone number masks and tokens, eliminating passwords and verification codes; this article explains the underlying PPP‑based network principles, the multi‑stage token exchange flow, and integration considerations across China’s three major operators.
This article explains the three major Redis cache issues—cache avalanche, cache breakdown, and cache penetration—how they can overload databases, and provides practical solutions such as high‑availability deployment, appropriate key expiration, local caches, mutex locks, empty‑object caching, request validation, and Bloom filters.
This guide explains how to create a seamless auto‑login feature by persisting a token in a cookie after the first successful login, storing the token in Redis, and validating it on subsequent visits, complete with Java Spring code examples.
This article explains why traditional relational databases falter under high‑concurrency loads, introduces caching as a solution, compares Redis distributed caching with local in‑process caching, and shows how combining them into a multi‑level cache can dramatically improve performance and reliability.
